stockbee-setup-fluency-trainer

Build a Stockbee-style setup model book from momentum-burst screener candidates, then update 3-day and 5-day forward outcomes with MFE/MAE, stop-hit status, outcome tags, and cohort statistics. Use when the user wants to study Stockbee Momentum Burst examples, track failed candidates, build setup fluency, review A/B setup quality, or convert screener outputs into a learning loop rather than immediate trade signals.

Quality

86%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Quality

Content

80%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-organized, concise skill body with executable commands and good progressive disclosure into real reference files. It is held back by a missing 'update' subcommand in the script and by the absence of explicit validation checkpoints for its batch/durable-state operations.

Suggestions

Register the 'update' subcommand in build_model_book.py (or correct the SKILL.md Step 2 command) so the documented workflow is fully executable as written.

Add explicit validation checkpoints to the workflow, e.g. 'after ingest, verify state/stockbee/model_book.jsonl is valid JSONL and the record count matches' and 'after update, confirm matured records increased', with a fix-and-retry loop.

Surface the offline-OHLCV vs FMP choice as a verification gate (e.g. confirm --prices-json covers all setup dates before trusting outcomes) to strengthen the batch feedback loop.

Dimension	Reasoning	Score
Conciseness	Lean and efficient: each workflow step is a copy-paste command block followed by a one-line note, with no padding or explanation of concepts Claude already knows. Every token earns its place.	5 / 5
Actionability	Provides concrete, executable CLI commands with real flags for ingest/summarize, but the Step 2 'update' command is referenced in SKILL.md while the script only registers the 'ingest' and 'summarize' subcommands — a real executable gap that prevents a 5.	4 / 5
Workflow Clarity	The 4-step sequence is clearly ordered, but for batch/durable-state operations (ingest, append-to-JSONL, outcome updates) there are no explicit validation/verification checkpoints or feedback loops, which the rubric caps at 3.	3 / 5
Progressive Disclosure	Clear overview with well-signaled, one-level-deep references to real bundle files (model_book_schema.md, outcome_tags.md, review_workflow.md), all confirmed present and appropriately split from the body.	5 / 5
	Total	17 / 20 Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, third-person description that names a specific domain, lists concrete capabilities, and provides an explicit multi-trigger 'Use when' clause. It is distinct and unlikely to conflict with other skills; the only minor gap is synonym/file-extension breadth in the trigger terms.

Dimension	Reasoning	Score
Specificity	Names the Stockbee Momentum Burst domain and lists multiple concrete actions — 'Build a Stockbee-style setup model book', 'update 3-day and 5-day forward outcomes with MFE/MAE, stop-hit status, outcome tags, and cohort statistics' — giving comprehensive coverage.	5 / 5
Completeness	Explicitly answers both what (build/maintain a model book with outcomes and cohort stats) and when via a concrete 'Use when the user wants to...' clause with several trigger conditions.	5 / 5
Trigger Term Quality	Strong natural triggers a user would say ('study Stockbee Momentum Burst examples', 'track failed candidates', 'build setup fluency', 'review A/B setup quality', 'convert screener outputs into a learning loop'), but not a full synonym/extension list that would warrant a 5.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear, narrowly-scoped niche (Stockbee Momentum Burst setup fluency / learning loop rather than trade signals) with distinct triggers and minimal overlap risk with other skills.	5 / 5
	Total	19 / 20 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: tradermonty/claude-trading-skills
Path: skills/stockbee-setup-fluency-trainer/SKILL.md
Commit: 51c7907

Reviewed: ~6 hours ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.